Chapter 4 Data Summary, EDA, & Initial Model Attempts
4.1 Introduction
I have three original datasets for analysis, both of which were collected from public websites between October 21 and 27, 2020.
- Yelp Reviews: 9,402 reviews for restaurants in Ottawa, which I believe includes all reviews available as of October 21. Each review includes:
- Business Name: The name the business is listed as operating under on Yelp. (Character)
- Reviewer Name: The screen name of the user who wrote the review. (Character)
- Review Date: The date the review was posted. (Character in mm/dd/yyyy format)
- Review Text: The full text of the review. (Character)
- Star Rating: The number of stars associated with the review (Integer from 1 to 5)
- Review URL: The URL from which the review was downloaded for traceability. (Character)
- Goodreads Reviews: 17,091 book reviews, culled from the first-page reviews of the “100 most-read books” in a number of genres. Each review includes:
- Book Title: The title of the book. (Character)
- Book Genre: The Goodreads-assigned genre of the book, e.g. “scifi” or “romance.” (Character)
- Book Author: The author of the book. (Character)
- Reviewer Name: The screen name of the user who wrote the review. (Character)
- Review Date: The date the review was posted. (Character in yyyy-mm-dd format)
- Review Text: The full text of the review. (Character)
- Star Text: Goodreads’ text equivalent for star ratings. (Character)
- Star Rating: The number of stars associated with the review (Integer from 1 to 5)
- Review URL: The URL from which the review was downloaded for traceability. (Character)
- Mountain Equipment Co-op (MEC) Reviews: 2,392 reviews for products for sale from MEC. Each review includes:
- Product Type: MEC’s categorization for the product (e.g. mittens, bicycle components.) (Character)
- Product Brand: The brand under which the product is marketed on MEC’s website. (Character)
- Product Name: The name of the product. (Character)
- Product ID: MEC’s internal product ID, used to call the API. (Character)
- Reviewer Name: The username of the review writer. (Character)
- Review Date: The date the review was left. (Character)
- Review Title: The title of the review. (Character)
- Review Text: The complete text of the review. (Character)
- Star Rating: The number of stars associated with the review. (Integer from 1 to 5)
In this section, I’ll take a look at these two datasets to get a feel for the star ratings and review text. I will consider each dataset in turn.
4.2 Goodreads
4.2.1 Star Ratings
The following histogram shows the overall distribution of star ratings. Reviews are overwhelmingly positive: there are move 5-star reviews than there are 1-, 2-, and 3-star reviews combined. This may make modeling more difficult, since there will be fewer low-star ratings to train our models.
reviews_gr %>%
ggplot() +
geom_bar(aes(x=rating_num)) +
theme_minimal() +
labs(title = "Goodreads Ratings: Rating Count, Overall",
x="Star Rating",
y=NULL)
The next histogram shows that the pattern is broadly consistent across genres. There are some minor differences: for example, graphic-novel and mystery reviews have nearly the same number of 4- and 5-star ratings, whereas nonfiction and romance novels show markedly more 5-star reviews than 4-star reviews. But for present purposes the overall pattern looks largely the same–for example, there are no U-shaped distributions, or exponential-type distributions with the opposite skew.
reviews_gr %>%
ggplot() +
geom_bar(aes(x=rating_num)) +
theme_minimal() +
labs(title = "Goodreads Ratings: Rating Count by Genre",
x = "Star Rating",
y=NULL) +
facet_wrap(facets = vars(genre))
However, if we look at the level of individual books, the distributions look a bit more interesting. All the histograms are unimodal, but some of them peak at 3 or 4. (Poor Brian K. Vaughan.)
top_6_books <- reviews_gr %>%
group_by(book_title) %>%
summarise(n = n()) %>%
slice_max(n=6, order_by=n, with_ties=FALSE) %>%
pull(book_title)
## `summarise()` ungrouping output (override with `.groups` argument)
reviews_gr %>%
filter(book_title %in% top_6_books) %>%
ggplot(aes(x = rating_num)) +
geom_histogram( binwidth=1, boundary=0.5, bins=5) +
facet_wrap(facets = vars(book_title)) +
theme_grey() +
labs(title = "Star Ratings for 6 of the Most-Reviewed Books",
subtitle = "Sampled randomly from across all genres.",
x = "Star Rating",
y = "# of Ratings")
4.2.2 Word Count
Turning to word count, the following graph shows the cumulative density of word counts in our review dataset. In other words, as word count increases on the x-axis, the y-axis shows us how many reviews have at most that many words. I have counted words here using unnest_tokens()
from the tidytext
package (as per Tidy Text Mining). There may be an easier way, but this worked!
We find that most reviews are very short: about 15,000 are below 500 words, and they go as short as one word. Some reviews are quite long, and one stretches out past 3,500 words.
wordcounts_gr <- reviews_gr %>%
select(comment) %>%
rowid_to_column() %>%
tidytext::unnest_tokens(word, comment) %>%
group_by(rowid) %>%
summarise(n = n()) %>%
arrange(n) %>%
mutate(id = 1,
cumdist = cumsum(id))
wordcounts_gr %>%
ggplot() +
geom_point(aes(y=cumdist, x=n)) +
theme_minimal() +
labs(title ="Goodreads Reviews: Cumulative Distribution of Word-Lengths",
x = "Word Length",
y = "# of Reviews")
This distribution may also make our modeling task more difficult. With so many short reviews it’s unlikely that they will have many words in common, and so a lasso regression at the word level may not work very well.
However, short reviews may still be useful for sentiment analysis. The following table shows the five shortest reviews, since I wanted to check and make sure it wasn’t a data error. One reviewer left a single word: “SUCKS.” Concise and informative.
wordcounts_gr %>%
arrange(n) %>%
head(5) %>%
pull(rowid) %>%
slice(reviews_gr, .) %>%
select(book_title,author_name, rating_num, comment) %>%
mutate(across(where(is.character), str_trunc, width=40)) %>%
knitr::kable(booktabs = T,
col.names = c("Book Title", "Book Author", "Stars", "Review"),
align = c("l","l","c","l"))
| Book Title | Book Author | Stars | Review |
|---|---|---|---|
| The Alchemist | Paulo Coelho | 1 | SUCKS. |
| The Mysterious Affair at Styles | Agatha Christie | 5 | Classic |
| Siddhartha | Hermann Hesse | 2 | Eh. |
| Treasure Island | Robert Louis Stevenson | 5 | ARRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR… |
| Logan Likes Mary Anne! | Gale Galligan | 4 | cool |
4.2.3 Reviewers
The following histogram shows that while most Goodreads users posted only a handful of reviews in our dataset, some posted over 50.
## `summarise()` ungrouping output (override with `.groups` argument)
reviewers_gr %>%
ggplot(aes(x=n)) +
geom_histogram() +
theme_minimal() +
labs(title = "Goodreads: Distribution of Reviews per User",
x = "# of Reviews",
y = "# of Users")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Looking at the following table, we can see that the top 10 reviewers all posted over 50 reviews, and one posted 95.
## # A tibble: 11 x 2
## names n
## <chr> <int>
## 1 Ahmad Sharabiani 95
## 2 Lisa 89
## 3 Matthew 63
## 4 jessica 61
## 5 Sean Barrs 59
## 6 Emily May 56
## 7 Michelle 53
## 8 Jennifer 52
## 9 Elyse Walters 51
## 10 Melissa <U+2665> Dog/Wolf Lover <U+2665> Martin 50
## 11 Nilufer Ozmekik 50
Out of curiosity (and as a check on our data quality), let’s investigate the 95 reviews from our top poster, Ahmad Sharabiani:
reviews_gr %>%
filter(names == "Ahmad Sharabiani") %>%
select(book_title, author_name, rating_num, comment) %>%
mutate (comment = str_trunc(comment, 80)) %>%
arrange(desc(author_name)) %>%
slice_head(n=10) %>%
knitr::kable(col.names = c("Book Title", "Book Author", "Stars", "Review"),
align = c("l","l","c","l"))
| Book Title | Book Author | Stars | Review |
|---|---|---|---|
| Romeo and Juliet | William Shakespeare | 5 | Romeo and Juliet = The Tragedy of Romeo and Juliet, William ShakespeareRomeo … |
| Othello | William Shakespeare | 4 | Othello = The Tragedy of Othello, William ShakespeareOthello (The Tragedy of … |
| Othello | William Shakespeare | 5 | The Tragedy of Othello, The Moor of Venice, William ShakespeareOthello is a t… |
| Lord of the Flies | William Golding | 4 |
|
| A Room of One’s Own | Virginia Woolf | 4 | A Room of One’s Own, Virginia WoolfA Room of One’s Own is an extended essay b… |
| Beowulf | Unknown | 5 | Beowulf, Anonymous Anglo-Saxon poetBeowulf is an Old English epic poem consis… |
| In Cold Blood | Truman Capote | 4 | In Cold Blood, Truman CapoteThis article is about the book by Truman Capote. … |
| The Bluest Eye | Toni Morrison | 4 |
|
| The Bell Jar | Sylvia Plath | 4 |
|
| The Shining | Stephen King | 4 | The Shining (The Shining #1), Stephen KingThe Shining is a horror novel by Am… |
Something looks a bit suspicious here. First, many books have more than one review (for example, Othello has 2 and The Catcher in the Rye has 3). Second, the reviews all seem to begin with the title of the book and a factual summary without much personality.
If we do a Google search for the opening text of Ahmad’s review for Farenheit 451, “Fahrenheit 451 is a dystopian novel by American”, we find that exact text in the first line of the book’s Wikipedia page. Google also suggests we look at Farenheit 451’s Goodreads page, which includes Ahmad’s review.
If we look at Ahmad’s review more closely, we see that it includes an English-language summary and then a lot of text in a non-Latin alphabet.
reviews_gr %>%
filter(names == "Ahmad Sharabiani" & book_title == "Fahrenheit 451") %>%
pull(comment) %>%
str_trunc(700)
## [1] "Fahrenheit 451, Ray BradburyFahrenheit 451 is a dystopian novel by American writer Ray Bradbury, published in 1953. Fahrenheit 451 is set in an unspecified city at an unspecified time in the future after the year 1960.Guy Montag is a \"fireman\" employed to burn houses containing outlawed books. He is married but has no children. One fall night while returning from work, he meets his new neighbor, a teenage girl named Clarisse McClellan, whose free-thinking ideals and liberating spirit cause him to question his life and his own perceived happiness. Montag returns home to find that his wife Mildred has overdosed on sleeping pills, and he calls for medical attention. ...<U+062A><U+0627><U+0631><U+06CC><U+062E> <U+0646><U+062E><U+0633><U+062A><U+06CC><U+0646> <U+062E><U+0648><U+0627><U+0646><U+0634>: <U+0631><U+0648>..."
Google Translate tells me the language is Persian, and the translated text includes a brief note–“Date of first reading: The third day of February 1984”–and then another summary of the book written in Persian. The text does not seem to have any actual review or opinion in it.
I’m not sure what’s going on here, but we have learned that: * Some users post a large number of reviews; * Some users post useless/non-review reviews, e.g. copy/pasting text from Wikipedia; and, * At least one super-poster posts such reviews.
This bears looking into more, since reviews that are copy/pasted from Wikipedia are unlikely to have any predictive value at all and may need to be identified and filtered out in pre-processing. These users may even be bots, especially given the short timeframe for the Goodreads dataset (see below).
4.3 Yelp
4.3.1 Star Ratings
Repeating the process for Yelp, this histogram shows the distribution of star ratings. Reviews are again very positive and show a similar distribution.
reviews_yelp %>%
ggplot() +
geom_bar(aes(x=rating_num)) +
theme_minimal() +
labs(title = "Yelp Ratings by Star",
x="Star Rating",
y=NULL)
The Yelp data didn’t include restaurant type, so we can’t do a genre-specific investigation as we did for Goodreads.
However, we can repeat the analysis where we look at star distributions for the top 6 businesses. Overall the distributions look the same, but here, finally, we get the first hint of bimodality in our distributions. Two restaurants, Sansotei Ramen and Shawarma Palace, have slight second peaks at 1 star. However, the overall story is the same and this could arguably be random fluctuations.
top_6_restos <- reviews_yelp %>%
group_by(business) %>%
summarise(n = n()) %>%
slice_max(n=6, order_by=n, with_ties=FALSE) %>%
pull(business)
## `summarise()` ungrouping output (override with `.groups` argument)
4.3.2 Word Count
As with the Goodreads data, most Yelp reviews are very short.
wordcounts_yelp <- reviews_yelp %>%
select(comment) %>%
rowid_to_column() %>%
tidytext::unnest_tokens(word, comment) %>%
group_by(rowid) %>%
summarise(n = n()) %>%
arrange(n) %>%
mutate(id = 1,
cumdist = cumsum(id))
wordcounts_yelp %>%
ggplot() +
geom_point(aes(y=cumdist, x=n)) +
theme_minimal() +
labs(title ="Yelp Reviews: Cumulative Distribution of Word-Lengths",
x = "Word Length",
y = "# of Reviews")
And again, let’s review the five shortest Yelp reviews in the table below. They seem to be genuine good-faith reviews that include helpful words, and so may be workable for our models.
wordcounts_yelp %>%
arrange(n) %>%
head(5) %>%
pull(rowid) %>%
slice(reviews_yelp, .) %>%
select(business,rating_num,comment) %>%
mutate(across(where(is.character), str_trunc, width=40)) %>%
knitr::kable(booktabs = T,
col.names = c("Business", "Stars", "Review"),
align = c("l","c","l"))
| Business | Stars | Review |
|---|---|---|
| Kallisto Greek Restaurant | 4 | Great takeout, service, ambiance and … |
| Bite Burger House | 4 | Delicious, juicy, interesting burgers… |
| BeaverTails | 4 | BeaverTails pastry..no words needed….. |
| Saigon Boy Noodle House | 3 | Very decent pho shop, well priced. |
| Supreme Kabob House | 5 | Excellent Afghani Food and Good Space |
4.3.3 Reviewers
The following histogram shows how many reviews were posted be users. Its distribution is similar to the one we found for Goodreads: most users posted only a few times, but some posted over 50.
## `summarise()` ungrouping output (override with `.groups` argument)
reviewers_yelp %>%
ggplot(aes(x=n)) +
geom_histogram() +
theme_minimal() +
labs(title = "Yelp: Distribution of Reviews per User",
x = "# of Reviews",
y = "# of Users")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Looking at our top-10 Yelp reviewers, the drop-off is quite a bit sharper than it was for Goodreads.
reviewers_yelp %>%
top_n(10, wt = n) %>%
knitr::kable(col.names = c("Name", "# Reviews"),
align = c("l","c"))
| Name | # Reviews |
|---|---|
| Jennifer P. | 78 |
| Amelia J. | 77 |
| Dawn M. | 51 |
| Samantha M. | 44 |
| Eric B. | 41 |
| Amanda B. | 35 |
| Coy W. | 34 |
| Drew K. | 27 |
| Spike D. | 25 |
| Amy B. | 23 |
The following table shows the first 10 reviews by our top reviewer, Jennifer P., in chronological order.
reviews_yelp %>%
filter(name == "Jennifer P.") %>%
select(date, business, rating_num, comment) %>%
mutate(date = lubridate::mdy(date),
comment = str_trunc(comment, 70)) %>%
arrange(date) %>%
slice_head(n=10) %>%
knitr::kable(booktabs = TRUE,
col.names = c("Date", "Business", "Stars", "Review"),
align = c("l","l","c","l"))
| Date | Business | Stars | Review |
|---|---|---|---|
| 2012-06-22 | Mr B’s-March House Restaurant | 4 | I never would have tried this restaurant had it not been recommende… |
| 2012-07-04 | Alirang Restaurant | 3 | I was here last week with my husband, my brother and his girlfriend… |
| 2013-01-24 | Corazón De Maíz | 4 | I can't believe that I walk by this place all the time, but… |
| 2013-06-24 | 222 Lyon Tapas Bar | 5 | This place is absolutely delicious, but man is it ever expensive! … |
| 2013-09-02 | Benny’s Bistro | 5 | I was visiting from out of town for my best friend's weddin… |
| 2013-09-20 | Art Is In Bakery | 4 | My husband and I were here for their Sunday Brunch recently with an… |
| 2013-11-22 | Gezellig | 3 | Sorry, I'm going to have to downgrade this place to 3 stars… |
| 2013-12-30 | Thai Coconut | 4 | I went here with my husband today for the lunch buffet. It was gre… |
| 2014-05-07 | Bite Burger House | 4 | I had an early dinner here with my husband recently. Bite Burger H… |
| 2014-07-07 | Pookies Thai | 4 | I went here for dinner recently with my husband on a whim. We&… |
These all seem to be good-faith restaurant reviews. And since this user has been active since 2012, to write 78 reviews they would have to write fewer than one per month. From this brief glance, we have no reason to think that Yelp users are posting insincere reviews.
However, I note that the reviews have some html junk in them: '
instead of an apostrophe, for example. These will need to be cleaned up before we use the data.
4.4 Mountain Equipment Co-op (MEC)
4.4.1 Star Ratings
This histogram shows the distribution of star ratings for MEC reviews. It’s broadly similar to the Yelp and Goodreads reviews, except there is a small second peak at 1 star.
reviews_mec %>%
ggplot() +
geom_bar(aes(x=rating_num)) +
theme_minimal() +
labs(title = "MEC Ratings: Rating Count, Overall",
x="Star Rating",
y=NULL)
If we break out the reviews by category, we can see that they all follow the same kind of exponential distribution except bicycle components.
reviews_mec %>%
ggplot() +
geom_bar(aes(x=rating_num)) +
theme_minimal() +
labs(title = "MEC Ratings: Rating Count by Product Category",
x="Star Rating",
y=NULL) +
facet_wrap(~product_type)
We can break the bicycle compoenents category down further by individual product. The facet wrap is messy, but we can clearly see that there are a few produts with anomalous spikes in 1-star ratings, and that ecah of these products has the word “tube” in the title.
reviews_mec %>%
filter(product_type=="bike-components") %>%
ggplot() +
geom_bar(aes(x=rating_num)) +
theme_minimal() +
labs(title = "MEC Ratings: Rating Count by Product",
subtitle = "Bicycle Components",
x="Star Rating",
y=NULL) +
facet_wrap(~product_name)
We can conclude that MEC’s reviews follow the same pattern as Yelp and Goodreads overall, except for bicycle inner tubes which have unusually high numbers of 1-star reviews. We should keep this in mind when modeling using the MEC data.
4.4.2 Word Counts
Most MEC reviews are very short. They look to be shortest of all three datasets, both in terms of the shape of the dsitribution and the maximum review lengths. We will see this below in a later section when we plot all three distributions at once.
wordcounts_mec <- reviews_mec %>%
select(comment) %>%
rowid_to_column() %>%
tidytext::unnest_tokens(word, comment) %>%
group_by(rowid) %>%
summarise(n = n()) %>%
arrange(n) %>%
mutate(id = 1,
cumdist = cumsum(id))
wordcounts_mec %>%
ggplot() +
geom_point(aes(y=cumdist, x=n)) +
theme_minimal() +
labs(title ="MEC Reviews: Cumulative Distribution of Word-Lengths",
x = "Word Length",
y = "# of Reviews")
If we look at the five shortest reviews, they all seem to be short but legitimate so we can be comfortable with our data quality.
wordcounts_mec %>%
arrange(n) %>%
head(5) %>%
pull(rowid) %>%
slice(reviews_mec, .) %>%
select(product_name,rating_num,comment) %>%
mutate(across(where(is.character), str_trunc, width=40)) %>%
knitr::kable(booktabs = T,
col.names = c("Business", "Stars", "Review"),
align = c("l","c","l"))
| Business | Stars | Review |
|---|---|---|
| MEC 700 x 23-25C Tube (48mm Presta Va… | 2 | Lasted 1 season basically disposable |
| Smartwool Liner Gloves - Unisex | 5 | Love smartwool products, the gloves a… |
| Scarpa Moraine Mid Gore-Tex Light Tra… | 5 | light, comfortable and good looking! … |
| Scarpa Moraine Mid Gore-Tex Light Tra… | 4 | good pair of shoes. lightweight but … |
| La Sportiva TC Pro Rock Shoes - Unisex | 5 | Flat stiff shoe. Perfect for vertical… |
4.4.3 Reviewers
As with the other datasets, it first appears that most users leave only a few reviews but there are some “super-users” who leave quite a few.
## `summarise()` ungrouping output (override with `.groups` argument)
reviewers_mec %>%
ggplot(aes(x=n)) +
geom_histogram() +
theme_minimal() +
labs(title = "MEC: Distribution of Reviews per User",
x = "# of Reviews",
y = "# of Users")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Upon closer inspection, however, we see that our largest “user” is NA, suggesting that most users leave a smallish number of reviews but that some leave reviews anonymously.
reviewers_mec %>%
top_n(10, wt = n) %>%
knitr::kable(col.names = c("Name", "# Reviews"),
align = c("l","c"))%>%
kableExtra::kable_styling(bootstrap_options = "striped")
| Name | # Reviews |
|---|---|
| NA | 76 |
| Matt | 9 |
| Chris | 8 |
| Mike | 8 |
| Ryan | 7 |
| John | 6 |
| Mark | 6 |
| VicCyclist40 | 6 |
| Dave | 5 |
| Paul | 5 |
| Steph | 5 |
The following table shows all 9 reviews by our top reviewer, Matt, in chronological order.
reviews_mec %>%
filter(user_name == "Matt") %>%
select(date, product_name, rating_num, comment) %>%
mutate(comment = str_trunc(comment, 70)) %>%
arrange(date) %>%
knitr::kable(booktabs = TRUE,
col.names = c("Date", "Product", "Stars", "Review"),
align = c("l","l","c","l"))
| Date | Product | Stars | Review |
|---|---|---|---|
| 2016-02-29 04:59:40 | Black Diamond Mercury Mitts - Men’s | 3 | I bought these to replace the BD mercury mitts I purchased 6 years … |
| 2017-08-23 20:16:05 | MEC Mallard -5C Down Sleeping Bag - Unisex | 5 | I usually use a bag with a hood but found my self feeling confined … |
| 2017-11-14 03:28:48 | Oboz Bridger Mid Bdry Hiking Shoes - Men’s | 5 | Bought these boots two years ago. Hiked up Sulphur Skyline in Jaspe… |
| 2018-01-20 21:55:36 | MEC Goto Fleece Gloves - Unisex | 5 | These are casual use gloves for me and I wear them around town when… |
| 2018-02-01 11:13:17 | Black Diamond Guide Gloves - Men’s | 2 | Very warm, but not very durable. Considering the cost, these gloves… |
| 2018-07-24 20:03:37 | MEC Creekside 0C Sleeping Bag - Unisex | 1 | I’ve used this bag twice and froze both times at temperatures betwe… |
| 2019-05-15 04:00:46 | Scarpa Kailash Trek Gore-Tex Hiking Boots - Men’s | 5 | While i have so far only logged one day of hiking in my new Scarpas… |
| 2019-06-11 00:19:21 | La Sportiva Finale Rock Shoes - Men’s | 5 | I’m relatively new to the sport and decided to go with these as my … |
| 2019-08-25 17:47:30 | MEC Reactor 10 Double Sleeping Pad - Unisex | 5 | I recently bought this mattress for car camping and it is incredibl… |
This looks like a legit usage pattern with real reviews. However, we should also spot-check some reviews assigned to NA:
reviews_mec %>%
filter(is.na(user_name)) %>%
select(date, product_name, rating_num, comment) %>%
slice_head(n=10) %>%
mutate(comment = str_trunc(comment, 70)) %>%
arrange(date) %>%
knitr::kable(booktabs = TRUE,
col.names = c("Date", "Business", "Stars", "Review"),
align = c("l","l","c","l"))
| Date | Business | Stars | Review |
|---|---|---|---|
| 2012-08-05 02:01:07 | SRAM PC-971 9 Speed Chain | 3 |
I used this chain on both my road and mountain bikes. It’s done fi… |
| 2013-09-23 01:57:46 | MEC 700 x 23-25C Tube (48mm Presta Valve) | 1 | Don’t waste your time with these. I’ve been through 4 this season a… |
| 2014-05-12 17:00:57 | MEC 700 x 23-25C Tube (48mm Presta Valve) | 1 | This is the third of these tubes I have had split down the seam. Th… |
| 2014-06-25 03:13:43 | MEC 700 x 23-25C Tube (60mm Presta Valve) | 1 | I’m afraid that I have to add my voice to the chorus of negative re… |
| 2014-10-04 02:21:33 | MEC 700 x 23-25C Tube (48mm Presta Valve) | 4 | I’m not sure where this chorus of negative reviews is coming from. … |
| 2014-10-25 21:28:29 | MEC 700X32-35C (27"x1 1/4) Tube Schrader Valve | 1 | I’ve bought two of these tubes and had to return both of them, I wi… |
| 2015-08-06 21:25:01 | MEC 700X32-35C (27"x1 1/4) Tube Schrader Valve | 3 | Have used three of these over a couple of years and the only one th… |
| 2015-11-16 19:45:33 | MEC 700 x 23-25C Tube (48mm Presta Valve) | 1 | I ride ten kilometres to work and ten kilometres home from work eve… |
| 2016-02-27 13:47:26 | SRAM PC-971 9 Speed Chain | 4 | I have been using PC971 chains for many years. Currently I do most … |
| 2016-06-22 21:38:52 | SRAM PC-971 9 Speed Chain | 4 | The chain that I have purchased from MEC is really a good chain. I … |
These also look like legitimate reviews, so it’s possible that these were legitimately left anonymously or that there was a data-parsing issue with the API.
4.5 Comparing Goodreads, MEC, and Yelp
4.5.1 Star Ratings
When we compare Yelp and Goodreads reviews by the number of star ratings, the distributions look very similar. There are fewer Yelp reviews, but the shape of the distribution looks like a scaled-down version of the Goodreads distribution. There are far fewer MEC reviews, and it looks like the distribution has a slight second peak at 1 star.
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## Joining, by = "rating_num"
## Joining, by = "rating_num"
compare_long <- compare %>%
pivot_longer(cols = c("gr", "yp","mc"), names_to = "source", values_to = "num")
compare_long %>%
ggplot() +
geom_col(aes(x=rating_num, y=num, group=source, fill=source), position = "dodge") +
theme_minimal() +
labs(title = "Goodreads, MEC, and Yelp Reviews: Total Counts by Rating",
x = "Star Rating",
y = "n",
fill = "Source") +
scale_fill_viridis_d(labels = c("Goodreads", "MEC", "Yelp"))
To get a better feel for how the distributions vary, we can plot the proportional breakdown of star reviews for each source. The following plot shows that the Goodreads and Yelp distributions track each other somewhat closely but the MEC reviews are quite different.
compare_long %>%
group_by(source) %>%
mutate(prop = num / sum(num)) %>%
ggplot() +
geom_col(aes(x=rating_num, y=prop, group=source, fill=source), position = "dodge") +
theme_minimal() +
labs(title = "Goodreads, MEC, and Yelp Reviews: Proportion of Counts by Rating",
x = "Star Rating",
y = "Proportion",
fill = "Source") +
scale_fill_viridis_d(labels = c("Goodreads", "MEC", "Yelp"))
We can use a standard Pearson’s Chi-squared test to see if the Goodreads and Yelp distributions differ meaningfully.
## Warning in chisq.test(compare$gr, compare$yp): Chi-squared approximation may be incorrect
##
## Pearson's Chi-squared test
##
## data: matrix(c(compare$gr, compare$yp), ncol = 5)
## X-squared = 8357.3, df = 4, p-value < 2.2e-16
We find that yes, we can reject the null hypothesis that there is no difference between the two distributions with a large amount of confidence. However, the two review distributions are still qualitatively similar, it’s not clear that the difference between them is large or meaningful–we could look into that later.
4.5.2 Word Counts
Out of interest, let’s also check the differences in word-count distributions between the three datasets. From the figure below, we can see that Yelp reviews tend to be much shorter than Goodreads reviews. Just by visual inspection, we can estimate that the 80th percentile Goodreads review is about 500 words, whereas the 80th percentile Yelp review is only about half of that. The MEC reviews are shortest of all.
wordcounts_all <- wordcounts_gr %>%
select(n, cumdist) %>%
mutate(source = "goodreads") %>%
bind_rows( wordcounts_yelp %>%
select(n, cumdist) %>%
mutate(source = "yelp")) %>%
bind_rows( wordcounts_mec %>%
select(n, cumdist) %>%
mutate(source = "mec"))
wordcounts_all %>%
group_by(source) %>%
mutate (prop = cumdist / max(cumdist)) %>%
ggplot() +
geom_point(aes(y=prop, x=n, colour = source)) +
labs(title = "Cumulative Distribution of Word Lengths",
subtitle = "Comparing Goodreads, MEC, and Yelp",
x = "Word Length",
y = "Cumulative Probability",
colour = "Source") +
scale_color_viridis_d(labels = c("Goodreads", "MEC", "Yelp")) +
theme_minimal()
To test for difference, we can confirm do a non-parametric Kolmogorov-Smirnov test to see if the Goodreads and Yelp distributions differ.
# pull the word lengths for goodreads into a vector
grd <- wordcounts_all %>%
filter(source == "goodreads") %>%
pull(n)
# pull the word lengths for yelp into a vector
ypd <- wordcounts_all %>%
filter(source == "yelp") %>%
pull(n)
# run KS test comparing the two vectors
ks.test(grd, ypd)
## Warning in ks.test(grd, ypd): p-value will be approximate in the presence of ties
##
## Two-sample Kolmogorov-Smirnov test
##
## data: grd and ypd
## D = 0.24968, p-value < 2.2e-16
## alternative hypothesis: two-sided
We can again reject the null hypothesis that there is no difference between the two distributions. We can hypothesize about why there might be a difference: Goodreads reviewers are writing about books, and so might be expected to be interested in expressing themselves through writing. Yelp reviewers, by and large, are interested in restaurants, and so may not put as much effort into writing full reports.
We might expect the difference in distributions to have an effect on our future modeling, since shorter reviews may contain less information.
4.6 Reviews Over Time
This section looks at how our review datasets change over time, to see how recent reviews are and if there are any trends in volume.
4.6.1 Goodreads
The following chart shows the monthly volume of reviews in the Goodreads dataset.
reviews_gr %>%
mutate(dates = lubridate::ymd(dates) %>% lubridate::floor_date("months")) %>%
group_by(dates) %>%
summarise(n = n()) %>%
ggplot(aes(x=dates,y=n)) +
geom_line() +
theme_minimal() +
labs(title = "Goodreads Reviews: Monthly Volume of New Reviews",
x = "Date",
y = "# of Reviews")
## `summarise()` ungrouping output (override with `.groups` argument)
While some reviews date from as far back as 2005, most reviews are from 2020 and the majority are from the past few months. However, it’s unlikely that this distribution represents an actual exponential growth in the number of reviews posted. Instead, recall that I collected reviews for the 100 most-read books in the past week across a few genres. In other words, I collected reviews from books that were being reviewed a lot at that moment in time, so my data collection is heavily biased towards more recent reviews. There may a trend in usage–for example, home-bound readers may be posting more reviews during COVID-19–but we can’t draw any conclusions from this distribution.
4.6.2 Yelp
The following chart shows the monthly volume of reviews in the Yelp dataset.
reviews_yelp %>%
mutate(date = lubridate::mdy(date) %>% lubridate::floor_date("months")) %>%
group_by(date) %>%
summarise(n = n()) %>%
ggplot(aes(x=date,y=n)) +
geom_line() +
theme_minimal() +
labs(title = "Yelp Reviews: Monthly Volume of New Reviews",
x = "Date",
y = "# of Reviews")
## `summarise()` ungrouping output (override with `.groups` argument)
Since I collected all Yelp reviews for restaurants in Ottawa, we can use this dataset to make statements about how review volumes have changed over time. We can see a steep decline in the early months of 2020, coinciding with the start of the COVID-19 pandemic and worldwide lockdowns. However, the volumes also tell an interesting story pre-COVID. From 2010 to 2015 we can see what looks like slow but steady growth, and then after 2015 usage increases dramatically. From 2015-2020 we can see what look like seasonal trends, but it looks like overall volumes stopped growing and may have started declining. In other words, Yelp may have been in trouble before the pandemic hit.
For our purposes, we can be satisfied that our restaurant review dataset spans a long period of time both pre- and post-COVID.
4.6.3 MEC
The following chart shows the monthly volume of reviews in the MEC dataset for each complete month. The data was collected in the first few days of November, so I have left November out.
reviews_mec %>%
mutate(date = lubridate::floor_date(date, "months")) %>%
group_by(date) %>%
summarise(n = n()) %>%
slice_head(n = nrow(.)-1) %>%
ggplot(aes(x=date,y=n)) +
geom_line() +
theme_minimal() +
labs(title = "MEC Reviews: Monthly Volume of New Reviews",
x = "Date",
y = "# of Reviews")
## `summarise()` ungrouping output (override with `.groups` argument)
We can expect several biases in the MEC data, so we will need to be cautious about making inferences from this time series. First, I collected MEC data from only a few product categories which may have seasonal trends (e.g. biking in the summer, snowshoeing in the winter). Second, MEC only lists products on its website if they’re currently for sale, so the maximum review age is limited by the longevity of MEC’s product lines. So we should expect to see a decay in review volume as we go further back in time caused by MEC naturally rotating its product line.
 This could correspond to a big drop in sales with the COVID lockdown and associated uncertainty, and then a bike spike in outdoor sporting goods as people tried to find socially distanced ways of entertaining themselves over the summer.
Out of curiosity, here are the 10 oldest reviews in our dataset:
## # A tibble: 10 x 3
## date product_name review_title
## <dttm> <chr> <chr>
## 1 2007-05-22 00:00:00 MEC V-Brake Cartridge Brake Pads I need more!!!
## 2 2007-08-12 00:00:00 MEC V-Brake Shoe/Pad Assembly Buy them once, love them forever
## 3 2007-09-05 00:00:00 SRAM PC-971 9 Speed Chain Great value
## 4 2008-02-28 00:00:00 MEC V-Brake Cartridge Brake Pads Decent brake pads
## 5 2008-11-02 00:00:00 MEC V-Brake Shoe/Pad Assembly Great product.
## 6 2008-12-16 00:00:00 MEC V-Brake Shoe/Pad Assembly Best value in a V-brake pad!
## 7 2008-12-18 00:00:00 Zamberlan Vioz GT Gore-Tex Backpacking Boots - Women's Mènent au sommet!
## 8 2009-01-15 00:00:00 SRAM PC-971 9 Speed Chain Decent deal on a higher end chain.
## 9 2009-07-21 18:37:26 SRAM PC-830 8 Speed Chain short life
## 10 2009-08-17 19:44:51 SRAM PC-830 8 Speed Chain Not impressed!
Not surprisingly, 9 out of 10 are for standard bicycle components that are more about function than fashion: it seems that MEC and SRAM have been offering the same brake pads and chains for more than 10 years.
And we can take a look at the first review for the Zamberlan boots:
reviews_mec %>%
filter(product_name=="Zamberlan Vioz GT Gore-Tex Backpacking Boots - Women's") %>%
slice_head(n=1) %>%
transmute(date = date,
comment = str_trunc(comment, 150))
## # A tibble: 1 x 2
## date comment
## <dttm> <chr>
## 1 2015-05-26 20:40:15 I've been using these Zamberlan Viozes for the past 4 years. I've owned 4 pairs in that time and I'm just about to start my 5th. I buy a pair every...
These boots seem to have been around for a while (and certainly seem to have committed fans), so we can be confident that these reviews are legit.
4.7 Proposed Next Steps
- Sentiment analysis
- Regression models
- LASSO regression to predict star rating from review text.
- Potential to use minimum review length as a parameter.
- Linear regression to predict star rating from review sentiment.
- LASSO regression to predict star rating from review text.
- Classification models
4.8 SessionInfo
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 18363)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_Canada.1252 LC_CTYPE=English_Canada.1252 LC_MONETARY=English_Canada.1252 LC_NUMERIC=C LC_TIME=English_Canada.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] vip_0.2.2 glmnet_4.0-2 Matrix_1.2-18 ggridges_0.5.2 discrim_0.1.1 tictoc_1.0 textrecipes_0.3.0 lubridate_1.7.9 yardstick_0.0.7 workflows_0.2.0
## [11] tune_0.1.1 rsample_0.0.8 recipes_0.1.13 parsnip_0.1.4 modeldata_0.0.2 infer_0.5.3 dials_0.0.9 scales_1.1.1 broom_0.7.0 tidymodels_0.1.1
## [21] tidytext_0.2.5 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4 readr_1.3.1 tidyr_1.1.1 tibble_3.0.3 ggplot2_3.3.2 tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 ellipsis_0.3.1 class_7.3-17 fs_1.5.0 rstudioapi_0.11 listenv_0.8.0 furrr_0.1.0 farver_2.0.3 SnowballC_0.7.0
## [10] prodlim_2019.11.13 fansi_0.4.1 xml2_1.3.2 codetools_0.2-16 splines_4.0.2 knitr_1.29 jsonlite_1.7.0 pROC_1.16.2 packrat_0.5.0
## [19] dbplyr_1.4.4 compiler_4.0.2 httr_1.4.2 backports_1.1.7 assertthat_0.2.1 cli_2.0.2 htmltools_0.5.0 tools_4.0.2 gtable_0.3.0
## [28] glue_1.4.1 naivebayes_0.9.7 rappdirs_0.3.1 Rcpp_1.0.5 cellranger_1.1.0 DiceDesign_1.8-1 vctrs_0.3.2 iterators_1.0.12 timeDate_3043.102
## [37] gower_0.2.2 xfun_0.16 stopwords_2.0 globals_0.13.0 rvest_0.3.6 lifecycle_0.2.0 future_1.19.1 MASS_7.3-51.6 ipred_0.9-9
## [46] hms_0.5.3 parallel_4.0.2 yaml_2.2.1 gridExtra_2.3 rpart_4.1-15 stringi_1.4.6 highr_0.8 tokenizers_0.2.1 foreach_1.5.0
## [55] textdata_0.4.1 lhs_1.0.2 hardhat_0.1.4 shape_1.4.5 lava_1.6.8 rlang_0.4.7 pkgconfig_2.0.3 evaluate_0.14 lattice_0.20-41
## [64] labeling_0.3 tidyselect_1.1.0 plyr_1.8.6 magrittr_1.5 bookdown_0.20 R6_2.4.1 generics_0.0.2 DBI_1.1.0 pillar_1.4.6
## [73] haven_2.3.1 withr_2.2.0 survival_3.1-12 nnet_7.3-14 janeaustenr_0.1.5 modelr_0.1.8 crayon_1.3.4 utf8_1.1.4 rmarkdown_2.3
## [82] usethis_1.6.1 grid_4.0.2 readxl_1.3.1 blob_1.2.1 reprex_0.3.0 digest_0.6.25 webshot_0.5.2 munsell_0.5.0 GPfit_1.0-8
## [91] viridisLite_0.3.0 kableExtra_1.1.0


From:DR.FRANK OBI<EMAIL_ADDRESS>Tel:234-8042137980 19/05/2004 We are making this contact with you because of the reliable information available to us concerning your reputation and that of your company. This business request may seem strange, but I crave your indulgence and pray you, view it seriously as we are convinced that you would be capable to provide us with a solution to a moneytransfer of Thirty Two Million, Five hundred thousand United State Dollars (US$32,500,000.00) First of all I am an accountant and Auditor of the Petroleum (special) Trust Fund (N.N.P.C) the Apex Department of the Oil Industry in Nigeria. I am also a member of its Foreign Contract Tenders Committee (FCTC). My principal Job/function has to do with the Appraisal and Approval of payment of contract awarded by this parastatal. In the course of my duties, I discovered various over inflated contract values totaling US$32.5Million. The said amount emanated from deliberate over invoicing of old projects executed by some foreign firms through some government officials.These officials have since been moved or retired following the inauguration of the new administration. After due consultation with key members of the Tenders Committee (FCTC), I was mandated to seek and arrange with a trusted foreign National who can assist us in transferring the above state amount out of Nigeria. Now that all the contracts have been paid following the completion and commissioning of the various contract, the above mentioned sum through approved is still floating. We decided to transfer this money to a safe and trusted bank accounts with a very reliable person to assist us after which the funds be share in the understated ratio. Your assistance is needed for two main reasons; (i) As government employees ,we are not permitted by the Nigeria law to operate foreignaccounts. (ii)Since the initial contracts were executed by the foreign firms, only a foreign firm can make claims or as a beneficiary for the purpose of transferring the money out of Nigeria. We have unanimously agreed to compensate you with 35% of the total sum ( US$32.5Million ) if you are willing to render us your assistance as regards the transfer .it was also agreed that 5% shall be set aside to offset incidental expenses that might be incurred by either of us course of offsetting this transfer. The remaining 60% shall be our due share. All necessary arrangements required for a speedy conclusion of this transaction have been put in place with top officials of the Nigeria Federal Ministry of Finance and the Central Bank of Nigeria to enable us conclude the whole transaction within a very shortspace of time. What is required of you is first to furnish us with the following through my email address. 1.) Bank details to be used (that is banks name and account number ) 2.) The beneficiary's name. 3.) Your private / official telephone and fax number. While promising you a smooth and hitch -free transfer of this fund into the account nominated by you, we want you to know that this is very sensitive transaction which should not be disclosed to any other party. This is very important because this deals involves some high ranking Nigeria Government Officials who would not want to be involved in any form of scandal.We however trust that this will be the beginning of a lasting business relationship between you and us as we shall appreciate your advice on the investment opportunities in your line of business or any other type of viable business in your country. If this deal interest you,reply me immediately through my personal E-mail<EMAIL_ADDRESS>or call me on my receiving number:234-8042137980 for more details. Best regards as we look forward to hearing from you soonest. Your Sincerely, DR FRANK OBI


19
Rejoicing in Heaven
1 After this I heard what seemed to be the loud voice of a great multitude in heaven, crying out,
“Hallelujah!
Salvation and glory and power belong to our God,
2 for his judgments are true and just;
for he has judged the great prostitute
who corrupted the earth with her immorality,
and has avenged on her the blood of his servants.”
3 Once more they cried out,
“Hallelujah!
The smoke from her goes up forever and ever.”
4 And the twenty-four elders and the four living creatures fell down and worshiped God who was seated on the throne, saying, “Amen.
The Marriage Supper of the Lamb
6 Then I heard what seemed to be the voice of a great multitude, like the roar of many waters and like the sound of mighty peals of thunder, crying out,
“Hallelujah!
For the Lord our God
the Almighty reigns.
7 Let us rejoice and exult
and give him the glory,
for the marriage of the Lamb has come,
and his Bride has made herself ready;
8 it was granted her to clothe herself
with fine linen, bright and pure”—
for the fine linen is the righteous deeds of the saints.
9 And the angel said to me, “Write this: Blessed are those who are invited to the marriage supper of the Lamb.” And he said to me, “These are the true words of God.”
10 Then I fell down at his feet to worship him, but he said to me, “You must not do that! I am a fellow servant with you and your brothers who hold to the testimony of Jesus. Worship God.” For the testimony of Jesus is the spirit of prophecy.
The Rider on a White Horse
11 Then I saw heaven opened, and behold, a white horse! The one sitting on it is called Faithful and True, and in righteousness he judges and makes war.
12 His eyes are like a flame of fire, and on his head are many diadems, and he has a name written that no one knows but himself.
13 He is clothed in a robe dipped in blood, and the name by which he is called is The Word of God.
14 And the armies of heaven, arrayed in fine linen, white and pure, were following him on white horses.
15 From his mouth comes a sharp sword with which to strike down the nations, and he will rule them with a rod of iron. He will tread the winepress of the fury of the wrath of God the Almighty.
16 On his robe and on his thigh he has a name written, King of kings and Lord of lords.
17 Then I saw an angel standing in the sun, and with a loud voice he called to all the birds that fly directly overhead, “Come, gather for the great supper of God,
18 to eat the flesh of kings, the flesh of captains, the flesh of mighty men, the flesh of horses and their riders, and the flesh of all men, both free and slave, both small and great.”
19 And I saw the beast and the kings of the earth with their armies gathered to make war against him who was sitting on the horse and against his army.
20 And the beast was captured, and with it the false prophet who in its presence had done the signs by which he deceived those who had received the mark of the beast and those who worshiped its image. These two were thrown alive into the lake of fire that burns with sulfur.
21 And the rest were slain by the sword that came from the mouth of him who was sitting on the horse, and all the birds were gorged with their flesh.


<<< Back
Submit >>>
Welcome back to the World of Math!
Your brother George is struggling with Math again. He is learning about medians, modes and averages. On a bonus problem on his homework, he is given a grid of numbers, and asked to calculate the median of the medians, the mode of the modes, and the average of the averages.
Input Specifications
The first line will contain N (N < 250, N will be odd).
The next N lines will contain N integers each, space-separated.
Output Specifications
The output will contain 3 integers on one line, the median of the medians, the mode of the modes, and the average of the averages.
The median is the middle number of a sequence when sorted in increasing order.
The mode is the most common element of a sequence, with ties broken by whichever element is larger.
The average is the sum of the numbers in the sequence divided by the length of the sequence.
Round the final average to 2 decimal places. If the final average is precise to fewer than 2 digits, pad on extra zeroes so that the final output has a decimal point followed by exactly two digits.
Subtasks
For 20% of the available 100 marks, N < 10.
For an additional 20% of the available 100 marks, N < 100.
Sample Input
Sample Output


Chemistry
Unit 5 Practice Test
-
What is Avagadro's Number?
- 5.2 × 1024
- 3.011 × 1027
- 6.022 × 1023
- 6.02 × 103
-
How many atoms are in 6.2 moles of aluminum?
- 27
- 6.022 × 1023
- 1.03 × 10-23
- 3.7 × 1024
-
Convert 3.55 moles NaCl to formula units.
- 3.55 × 1021
- 1.7 × 1023
- 58.5
- 2.14 × 1024
-
How many moles are represented by 11.5 g of C2H5OH?
- 0.25 mol
- 529.81 mol
- 1.91 × 10-23 mol
- 6.92 × 1024 mol
-
What is the mass of 9.30 moles of SiH4?
- 5.6 × 1024 g
- 0.29 g
- 298.7 g
- 32.12 g
-
How many atoms of hydrogen are in 12.6 moles of water?
- 4.8 × 1022
- 5.1 × 1024
- 3.14 × 1015
- 7.6 × 1024
-
What is the molar mass of aluminum oxide (Al2O3)?
- 43 g/mol
- 102 g/mol
- 72.5 g/mol
- 59 g/mol
-
What would the mass of 5.3 × 1022 formula units of calcium iodide (CaI2)?
- 26 g
- 6.02 × 1023 g
- 1.09 × 1021 g
- 293.9 g
-
All of the following are types of reactions EXCEPT
- Single-Replacement
- Synthesis
- Decomposition
- Net Ionic
-
What type of reaction is the equation
Zn(s) + CuSO4(aq) → ZnSO4(aq) + Cu(s) ?
- Double-Replacement
- Synthesis
- Single-Replacement
- Decomposition
-
What type of reaction is the equation
2NaOH(aq) + MgSO4(aq) →
Na2SO4(aq) + Mg(OH)2(s) ?
- Double-Replacement
- Synthesis
- Single-Replacement
- Decomposition
-
What type of reaction is the equation
4Al + 3O2 → 2Al2O3 ?
- Double-Replacement
- Synthesis
- Single-Replacement
- Decomposition
-
What type of reaction is the equation
CuCO3 → CuO + CO2 ?
- Double-Replacement
- Synthesis
- Single-Replacement
- Decomposition
-
What drives double-replacement reactions?
- Water
- Precipitate formation
- Solubility
- Molar mass
-
Which of the following would be a precipitate?
- MgCO3
- NaOH
- MgSO4
- H2O
-
Predict the products of the following reactants:
MgSO4(aq) + LiMnO4(aq)
- Not enough information given
- Li2S(aq) + 3O2(g) + MgO2(s)
- Li2Mg(s) + SO4(MnO4)2(aq)
- Mg(MnO4)2(s) + Li2SO4(aq)
-
What is an empirical formula?
- The complete molecular ratio of a compound, or many compounds
- A formula that conquers others
- The lowest whole-number ratio of a compound
- The quotient ratio between two chemical formulae
-
What is the difference between a complete ionic equation and a net ionic equation?
- Complete ionic equations do not have spectator ions
- Net ionic equations do not have spectator ions
- Complete is molecular, net is empirical
- They are spelled differently
-
Which of the following equations is balanced correctly?
- MgBr2(s) + O2(g) → 2MgO(s) + Br2(g)
- C6H12O6(s) + 6O2(g) → 6CO2(g) + 6H2O(l)
- 2C6H6(s) + 9O2(g) → 6H2O + 12CO2
- MnO2(s) + Sn(OH)4(aq) → Mn(OH)2(aq) + SnO4(s)
-
How many atoms are in one mole of H2 gas?
- 1.204 × 1024
- 3.011 × 1023
- 6.022 × 1023
- 21


Introduction
Otto is an event bus designed to decouple different parts of your application while still allowing them to communicate efficiently.
Forked from Guava, Otto adds unique functionality to an already refined event bus as well as specializing it to the Android platform.
Usage
Otto is designed with Android-specific use cases in mind and is intended for use as a singleton
(though that is not required). These examples assume you have an
instance in the variable bus
obtained through injection or another appropriate mechanism.
Publishing
Event publishing is the most important part of the bus as it allows you to tell subscribers that an action has occurred. An instance of any class may be published on the bus and it will only be dispatched to subscribers for that type.
To publish a new event, call the post
method:
bus.post(new AnswerAvailableEvent(42));
Posting to the bus is a synchronous action so when program execution continues it is guaranteed that all subscribers have been called.
Subscribing
Subscription is the complement to event publishing—it lets you receive notification that an event
has occurred. To subscribe to an event, annotate a method with @Subscribe
. The method should
take only a single parameter, the type of which will be the event you wish to subscribe to.
To listen for the event published in the previous section we would need the following:
@Subscribe public void answerAvailable(AnswerAvailableEvent event) { // TODO: React to the event somehow! }
The name of the method can be anything you like. The annotation, single argument, and public accessor are all that is required.
In order to receive events, a class instance needs to register with the bus. If this
refers
to an instance of the class in which the previous method was present, we can register using the
following:
bus.register(this);
Registering will only find methods on the immediate class type. Unlike the Guava event bus, Otto will not traverse the class hierarchy and add methods from base classes or interfaces that are annotated. This is an explicit design decision to improve performance of the library as well as keep your code simple and unambiguous.
Remember to also call the unregister
method when appropriate. See the sample application
included in the download for a complete example.
Producing
When subscribing to events it is often desired to also fetch the current known value for specific events (e.g., current location, active user, etc.). To address this common paradigm, Otto adds the concept of 'Producers' which provide an immediate callback to any subscribers upon their registration.
To create a producer, annotate a method with @Produce
. The method should take no parameters
and its return type will be used as the type of event for which it can produce initial values. If we are
keeping track of the last answer event somewhere from above we can register to produce its initial
value:
@Produce public AnswerAvailableEvent produceAnswer() { // Assuming 'lastAnswer' exists. return new AnswerAvailableEvent(this.lastAnswer); }
Producers, like subscribers, must also be registered:
bus.register(this);
When registering, the producer method will be called once for each subscriber previously registered for the same type. The producer method will also be called once for each new method that subscribes to an event of the same type.
You may only have one producer per event type registered at a time on a bus.
Thread Enforcement
Since at times it may be ambiguous on which thread you are receiving callbacks, Otto provides an enforcement mechanism to ensure you are always called on the thread you desire. By default, all interaction with an instance is confined to the main thread.
// Both of these are functionally equivalent. Bus bus1 = new Bus(); Bus bus2 = new Bus(ThreadEnforcer.MAIN);
If you are not concerned on which thread interaction is occurring, instantiate a bus instance with
ThreadEnforcer.ANY
. You can also provide your own implementation of the
ThreadEnforcer
interface if you need additional functionality or validation.
 It is compatible with all versions of Android and has no minimum SDK requirement. You
can download the latest .jar on the GitHub project by clicking the "Download" button.
If you are a Maven user, you can also fetch Otto from the central repositories by including the following
dependnecy in your application's pom.xml
:
<dependency> <groupId>com.squareup</groupId> <artifactId>otto</artifactId> <version>(insert latest version)</version> </dependency>
Once you've installed Otto, add the following lines to your proguard-project.txt file:
-keepclassmembers class ** { @com.squareup.otto.Subscribe public *; @com.squareup.otto.Produce public *; }
This ensures your annotated methods aren't removed by ProGuard.
Contributing
If you would like to contribute code to Otto you can do so through GitHub by forking the repository and sending a pull request.
When submitting code, please make every effort to follow existing conventions and style in order to keep
the code as readable as possible. Please also make sure your code compiles by running mvn clean
verify
. Checkstyle failures during compilation indicate errors in your style and can be viewed in the
checkstyle-result.xml
file.
Before your code can be accepted into the project you must also sign the Individual Contributor License Agreement (CLA).
License
Copyright 2012 Square, Inc. Copyright 2010 Google, Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.


They Came From Within
a PAL-Trans American release written and directed by David Cronenberg produced by Ivan Reitman
stars: Barbara Steele, some other people who are probably dead by now rated: R for gore, violence, rape, nudity, intense situations
availabilty: Vestron Video (out of print, I would assume - probably rentals only) recommended: very highly
(c) 1998 Doug Mosurak
"They Came From Within" is a rough, untidy little horror film which marks one of the first recognized feature-length appearances by cult Canadian writer-director David Cronenberg, last heard from for his controversial adaptation of J.G. Ballard's "Crash" (1997). There are actually quite a few things in common between the two films: the recurrent sex-equals-death theme that's so prevalent in many of his films (especially "Rabid", "Dead Ringers", and "Videodrome"), scenes well-paced and designed to shock the audience out of their skins, and heavy Ballard influences (while "Crash" comes directly from a Ballard work, "Within" appropriates a good number of settings and plot devices from Ballard's 1973 novel, "High-Rise").
Watching "They Came From Within" in 1998, it really becomes evident that Cronenberg was designing a film that's as much of an homage to the sci-fi and horror films of the '50s as it is to the conventions he brought to the genre himself. It's also one of his most watchable films, and avoids a lot of the dreariness that many of his later films, like "Crash" and "Naked Lunch", were chided for.
"They Came From Within" takes place on Starliner Island, a state-of-the-art apartment complex outside of Montreal which offers its tenants all of the conveniences of city life, without the hassle of other people. From the get-go, it becomes perfectly clear that, even without the presence of the malevolent parasites that do indeed come from within, living in this building is a pretty warped place.  Nearly everyone in the complex is completely dispassionate towards life in general, and from the few glimpses of the tenants we see, it can easily be alluded that the excesses of '70s "modern living" in the wake of '60s free love are clearly in effect. The security guards at the front desk even carry guns "because all the other ones do". The building is a closed system, stagnating on the line between depravity and contempt.
The early scenes are comprised of a slideshow of the apartment's many features, which is a fairly effective device for getting the basic elements of the story out of the way. In one of the film's most intense sequences, Cronenberg juxtaposes a young couple entering the building with a fierce struggle between an older man and a young girl in a school uniform. It's unclear as to why the struggle is going on, or who indeed is the aggressor, but in the end, the man knocks the girl unconscious, rips off her clothes, and proceeds to cut her open with a scalpel and pour sulfuric acid into her body cavity. He then slashes his throat and is heard hitting the floor. It's a really effective, masterful sequence that equates a gender battle with freakish, clinical behavior, thus setting the pace for the rest of the film.
As it turns out, the man who gutted the girl is a researcher and biologist who was working on a new breed of parasite that could duplicate the functions of human organs. Hey, at least it sounds like a plausible idea. But of course, the parasites are really nasty creatures that do all the things that parasites do (slowly destroy their hosts while replicating inside of them), and do in a very gory fashion. The parasites also cause their hosts to engage in extremely compulsive and violent behavior. The girl was the test subject and got busy with several men on Starliner Island.   Soon the parasites are in the building's sewage system, and making their way inside several of the people dwelling there. And it's up to Dr. Hobbs, the facility's medical examiner, to try to figure out what's going on before it's too late.
This simple plot device gives Cronenberg the leeway to make an exceptional low-rent horror picture and in turn exploit just about any perversion you could think of.  It's obvious that Cronenberg is trying to put the audience on edge throughout the film, but by erasing each step with semi-humorous situations, he's able to desensitize viewers and push them even further. In creating an almost comical compulsion in the infected characters, Cronenberg also strikes a chord of the limits of human nature, and illustrates a phallocentric society that blurs primal behavior with regular human interactions. As a parasite makes its way out of a bathtub drain and between actress Barbara Steele's legs, this parallel cannot become more clear.
Cronenberg makes use of some excellent source material, using the film to pay homage to Don Siegel's "Invasion of the Body Snatchers", George Romero's "Night of the Living Dead", and "Village of the Damned" (as there seems to be some sort of unified mind between the infected characters). In fusing these films with the art-porn he created in films like "Stereo" (1969), Cronenberg ups the ante for levels of sex and violence in films up to that point (a process that had already begun with the advent of the '70s hardcore porn industry, and the works of folks like Sam Peckinpah, Tobe Hooper, and Sean Cunningham). In keeping with most of his films, there is not a happy ending (or maybe there is, depending on which team you're on), but the complete downer rush of films like "Rabid" (in which Santa Claus is blown away by National Guardsmen) and "Videodrome" (in which the protagonist kills himself in the end) isn't all that present here.
If you can stomach the gore and aren't offended by the numerous sexual situations in the film, I'd say peep it - it's a very good introduction to the bleak views of human nature as presented by Cronenberg throughout his body of work. Hopefully this film (and many of the other gaping holes in the American-International film library) will make its way back onto video sometime really soon.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Hostname
Username
Password
Port
Interval
Auth
Login
CRAMMD5
SaslOAuth
Secure?
Skip Cert Validating?
Program to open on pressed
No file...
Choose file...


Press space to fire, WASD or arrow keys to move. Asteroids are worth 100 pts, Bolides are worth 500 pts.
Gain points equal to the current level times 100 to advance to the next level and increase the difficulty.
If you touch an asteroid, you will lose a life unless you can correctly answer a question. Lose all your lives, and the game ends.


and the HIV for with are {background margin {border-style px;} that who #ffffff;} this health COVID- time []. prevention testing their use PrEP have our but care px; those {font-family community font-size from living not people these will COVID-. MH; The also double; prophylaxis viral AIDS Center HIV/AIDS Now STIs access all already border-color border-width can clinicians disease how most need now primary resources sexual solid; transmission treatment {font-style {font-weight Commons Creative Miami This University affected among bold clinical courier; em; free identify including individuals know material needed new other pre-exposure predictable pt;} public tests undetectable work


This is a backup of the original GOIME 500 by Htwins central, please do not use this backup unless the original site is down


 Instead, as I contemplate my next move, I’m listening intently as I stage each option in my mind for a faint grumbling in my stomach. “Stay Hungry. Stay Foolish.” Steve Jobs
A Full stuck developer who focuses on wrinting clean ,elegant and efficient code.
A UI/UX Designer with a passion for designing beautiful and functional user experiences.
Check my resume


UFC Moscow Prelim Breakout Star: Pannie Kianzad
by Daniel VreelandNov 30, 2018; Las Vegas, NV, USA; Pannie Kianzad (red) during fighter introductions before a match against Macy Chiasson (not seen) during a featherweight bout at The Pearl. Mandatory Credit: Stephen R. Sylvanie-USA TODAY Sports
The Prelim Breakout Star is a once per event article that helps fans get to know those who will be entertaining them from the undercard. The intention of the article is not to bring attention to the most talented fighter on the preliminary card, but rather one that the common fan may have overlooked or underestimated.
There’s a ton of Russia names flooding this card in Moscow, but the name we’re the most focused on comes from another place in Europe. She may not have any wins yet in the UFC, but her potential is clearly there.
Pannie Kianzad
Nickname – Banzai
Affiliation – Arte Suave
From – Helsingborg, Sweden
Height – 5’7″
Weight – 135 lbs (Bantamweight)
Record – 11-5 (0-2 UFC)
What makes her impressive
Over the course of her 16 fight career, Kianzad has shown an aptitude in controlling her opponents. Whether it is against the cage or after a takedown, the Iranian-born fighter has always been able to dictate where the fight goes, which usually benefits her well on the judges’ scorecards. Even in her recent loss to Julie Avila, she was able to take Avila down and control her for a bit from the top. Although it wasn’t ultimately successful, it showed that those abilities clearly translate at the UFC level as well.
Why she has been overlooked
The 0-2 record in the UFC is certainly not something that catches a fan or analyst’s eye in a positive way. However, there really is a solid reason for both losses.
In the first loss, a TUF Finale submission loss to Macy Chiasson, Kianzad was fighting at featherweight. Although she and her opponent weighed in at the same weight, and both of them eventually made their way down to bantamweight, Chaisson is a much more natural 145er. As a result, it was hard for Kianzad to implement her gameplan and control the cage as she was giving up near half a foot worth of reach.
In her second bout, Kianzad took the fight with the aforementioned Avila on short notice. With only two weeks to prepare for a fight halfway across the world, it’s not surprising that Kianzad didn’t look as impressive as expected.
What makes this a good match-u p
Jessica-Rose Clark is making her bantamweight debut after three bouts in the UFC’s flyweight division. Although she went 2-1 in those bouts, she had a difficult time with the grappling of the women in her division. In those three bouts, Clark only defended 57% of the takedown attempts that came her way. This includes allowing two takedowns to Bec Rawlings, who isn’t exactly known for her wrestling prowess. This, along with the size advantage being in Kianzad’s corner, should make it easy for “Bonzai” to control this fight and win a decision.
Prelim Breakout Star Rec ord: 186-79-1 (2 NC) (7 DNF)
Shop all things UFC at Amazon!
(The MMA Manifesto is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to amazon.com)


| Symbol | Status | Start Date | End Date | Strike Price | Payout | Purchase Price | |
|---|---|---|---|---|---|---|---|
| BCH | Open | 1/1/2017 | 1/1/2018 | $14,000 | $1,000 | $500 | view |
| BCS | Open | 1/1/2017 | 1/1/2018 | $2,000 | $600 | $400 | view |
| ETH | Active | 1/1/2017 | 1/1/2018 | $1,000 | $1,000 | $500 | view |
| XRP | Open | 1/1/2017 | 1/1/2018 | $1 | $1,000 | $500 | view |
| STR | Open | 1/1/2017 | 1/1/2018 | $.50 | $80 | $120 | view |
| BCH | Open | 1/1/2017 | 3/1/2018 | $14,000 | $2,000 | $500 | view |


Enter your info here!
Full name:
Title:
Phone:
Email:
@hackbeanpot.com
Paste this into Gmail!
you gotta press submit first tho


| JIRA | Summary | Priority | Component | Reporter | Contributor |
|---|---|---|---|---|---|
| HBASE-3783 | hbase-0.90.2.jar exists in hbase root and in 'lib/' | Blocker | build | Lukas | stack |
| JIRA | Summary | Priority | Component | Reporter | Contributor |
|---|---|---|---|---|---|
| HBASE-3717 | deprecate HTable isTableEnabled() methods in favor of HBaseAdmin methods | Trivial | Client | David Buttler | David Buttler |
| HBASE-3071 | Graceful decommissioning of a regionserver | Major | . | stack | stack |
| HBASE-3746 | Clean up CompressionTest to not directly reference DistributedFileSystem | Major | . | Todd Lipcon | Todd Lipcon |
| HBASE-3747 | [replication] ReplicationSource should differanciate remote and local exceptions | Minor | . | Jean-Daniel Cryans | Jean-Daniel Cryans |
| HBASE-3652 | Speed up tests by lowering some sleeps | Critical | . | Jean-Daniel Cryans | Jean-Daniel Cryans |
| HBASE-3773 | Set ZK max connections much higher in 0.90 | Blocker | . | Jean-Daniel Cryans | Jean-Daniel Cryans |
| HBASE-3767 | Improve how HTable handles threads used for multi actions | Major | . | Jean-Daniel Cryans | Jean-Daniel Cryans |
| HBASE-3795 | Remove the "Cache hit for row" message | Trivial | . | Jean-Daniel Cryans | Jean-Daniel Cryans |
| HBASE-3798 | [REST] Allow representation to elide row key and column key | Minor | . | Andrew Purtell | Andrew Purtell |
| HBASE-3580 | Remove RS from DeadServer when new instance checks in | Major | . | Jean-Daniel Cryans | Jean-Daniel Cryans |
| HBASE-3818 | docs adding troubleshooting.xml | Minor | . | Doug Meil | Doug Meil |
| HBASE-2470 | Add Scan.setTimeRange() support in Shell | Trivial | . | Lars George | Harsh J |
| HBASE-3634 | Fix JavaDoc for put(List<Put> puts) in HTableInterface | Trivial | Client | Lars George | Harsh J |
| HBASE-3805 | Log RegionState that are processed too late in the master | Minor | . | Jean-Daniel Cryans | Jean-Daniel Cryans |
| HBASE-3695 | Some improvements to Hbck to test the entire region chain in Meta and provide better error reporting | Major | util | Marc Limotte | Marc Limotte |
| HBASE-3536 | [site] Add book all-in-one-page | Major | . | stack | stack |
| HBASE-3860 | HLog shouldn't create a new HBC when rolling | Critical | . | Jean-Daniel Cryans | Jean-Daniel Cryans |
| JIRA | Summary | Priority | Component | Reporter | Contributor |
|---|---|---|---|---|---|
| HBASE-3712 | HTable.close() doesn't shutdown thread pool | Major | . | Ted Yu | Ted Yu |
| HBASE-3734 | HBaseAdmin creates new configurations in getCatalogTracker | Major | . | Jean-Daniel Cryans | Jean-Daniel Cryans |
| HBASE-3756 | Can't move META or ROOT from shell | Critical | . | stack | stack |
| HBASE-3740 | hbck doesn't reset the number of errors when retrying | Major | . | Jean-Daniel Cryans | Jean-Daniel Cryans |
| HBASE-3750 | HTablePool.putTable() should call tableFactory.releaseHTableInterface() for discarded table | Major | Client | Ted Yu | Ted Yu |
| HBASE-3755 | Catch zk's ConnectionLossException and augment error message with more help | Major | . | Jean-Daniel Cryans | Jean-Daniel Cryans |
| HBASE-3722 | A lot of data is lost when name node crashed | Major | master | gaojinchao | gaojinchao |
| HBASE-3771 | All jsp pages don't clean their HBA | Blocker | . | Jean-Daniel Cryans | Jean-Daniel Cryans |
| HBASE-3708 | createAndFailSilent is not so silent; leaves lots of logging in ensemble logs | Major | Zookeeper | stack | Dmitriy V. Ryaboy |
| HBASE-3744 | createTable blocks until all regions are out of transition | Critical | master | Todd Lipcon | Ted Yu |
| HBASE-3800 | If HMaster is started after NN without starting DN in Hbase 090.2 then HMaster is not able to start due to AlreadyCreatedException for /hbase/hbase.version | Major | master | gaojinchao | Andrew Purtell |
| HBASE-3539 | Improve shell help to reflect all possible options | Trivial | shell | Lars George | Harsh J |
| HBASE-3817 | HBase Shell has an issue accepting FILTER for the 'scan' command. | Trivial | shell | Harsh J | Harsh J |
| HBASE-3749 | Master can't exit when open port failed | Major | master | gaojinchao | gaojinchao |
| HBASE-3794 | TestRpcMetrics fails on machine where region server is running | Major | test | Ted Yu | Alex Newman |
| HBASE-3741 | Make HRegionServer aware of the regions it's opening/closing | Blocker | . | Jean-Daniel Cryans | Jean-Daniel Cryans |
| HBASE-3597 | ageOfLastAppliedOp should update after cluster replication failures | Major | Replication | Otis Gospodnetic | Jean-Daniel Cryans |
| HBASE-3821 | "NOT flushing memstore for region" keep on printing for half an hour | Major | regionserver | zhoushuaifeng | zhoushuaifeng |
| HBASE-3848 | request is always zero in WebUI for region server | Minor | regionserver | gaojinchao | gaojinchao |
| JIRA | Summary | Priority | Component | Reporter | Contributor |
|---|---|---|---|---|---|
| HBASE-3846 | Set RIT timeout higher | Critical | . | Jean-Daniel Cryans | Jean-Daniel Cryans |


--- layout: post title: Day 3! date: 2018-11-07 22:28:35.000000000 +05:30 type: post parent_id: '0' published: true password: '' status: publish tags: [Daily Logs] meta: timeline_notification: '1541609919' _rest_api_published: '1' _rest_api_client_id: "-1" _publicize_job_id: '24020127042' author: login: priyankasaggu119 email<EMAIL_ADDRESS>display_name: priyankasaggu119 first_name: '' last_name: '' permalink: "/2018/11/07/day-3/" ---
November 7, 2018
A very Happy Diwali to everyone. 🙏 🎇 ✨
Today was a festive day and so, was busy with family for the entire time. Nothing new happened but then the whole family together is in itself a moment of joy and celebration for us (at least for folks like me whose every family member lives in different places) :) .
For the logs:
and nothing more. Helped Mommy with household chores, cooking and Daddy with Pooja preparations and that's it.
Tomorrow again is a day of celebration for the family. Ah, not Goverdhan Pooja, but then will let you know in next one ;) .
Till then, take care. o/


Home
Calendar
Progress
Data
Your Calendar
Hi [User]!...
Planned events for the week of XX/XX/XXXX:
Workouts here.


This module allows to find population specific variants by individual count. This module filters the variants at differential percentage of individuals between two populations defined in the PED file. Briefly, the user could filter variants of specific genotype that are shared by atleast X percentage of individuals in Population1. These variants are further compared to the Population2 i.e. filters for those varaints of a specific genotype which are present at a user specified maximum percentage of individuals in Population2
Select Population1: Population names to be chosen as Population1. Multiple population names can be selected.
Chose Gender: Enter the gender of the population to be queried.
Minimum MAF: Enter the minimum minor allele frequency of the variants in population1 to be retrieved.
Select Population2: Population names to be chosen as Population2. Multiple population names can be selected.
Chose Gender: Enter the gender of the population to be queried.
Maximum MAF: Enter the maximum minor allele frequency of the variants in population2.
Required columns in PED file:IndividualID, Population and Gender fields are mandatory for population module while other optional fields can be present. Gender could be coded as male/female or 1 for males and 2 for females and NA if unavailable.


--- layout: default ---
Hi! I'm Lauri Pesonen and I'm a software consultant who helps innovative technology companies adopt Amazon's AWS Lambda as their application backend.
AWS Lambda offers a number of advantages:
I can help you design and implement an AWS Lambda -based backend for your application. I can also review your existing backend and offer guidance on how to migrate it to AWS Lambda.
Click on the button below, send me a quick email and we'll have a chat to discuss your project.


{% extends "layout.html" %} {% block content %} Wazimap Ward Data {% if ward %} {% else %} {% if missing %} Hmm, we couldn't find your address or ward. Please try again. {% endif %} Enter your address: Please include your suburb name in the address where possible, putting just the city may lead to a crash. Or, use your location: Sorry, doesn't work in Chrome Locate {% endif %} {% endblock content %}
Hmm, we couldn't find your address or ward. Please try again.
Please include your suburb name in the address where possible, putting just the city may lead to a crash.


Bootstrap (currently v{{ site.current_version }}) has a few easy ways to quickly get started, each one appealing to a different skill level and use case. Read through to see what suits your particular needs.
Compiled and minified CSS, JavaScript, and fonts. No docs or original source files are included.
Source Less, JavaScript, and font files, along with our docs. Requires a Less compiler and some setup.


{{#with profile}}
{{#if addressBook}}
Choose shipping & billing address
{{else}}
Create your default address
{{/if}}
Select a shipping address
{{#each addressBook}}
{{fullName}}
{{address1}}
{{address2}},
{{city}}, {{region}} {{postal}} {{country}}
{{phone}}
{{/each}}
Select a billing address
{{#each addressBook}}
{{fullName}}
{{address1}}
{{address2}},
{{city}}, {{region}} {{postal}} {{country}}
{{phone}}
{{/each}}
{{/with}}


Thank you
{% blocktrans with account_name=purchase.account_name %}Your EOS Account {{account_name}} was created successfully.
{% endblocktrans %}
{% trans "What's next?" %}
{% blocktrans with account_name=purchase.account_name %} You can check out your new account in the EOS Block Explorer{% endblocktrans %}
{% trans "Please consider voting for" %} eosvibesbloc
{% trans 'If you have any questions, please visit our Telegram group.' %}


Welcome to the game of Hand-Cricket
TOSS
Choose:
Odd
or
Even
User :
0
CPU :
0
User's Choice :
-
CPU's Choice :
-
Choose one of the following:
1
2
3
4
5
6
You won the toss. Do you want to Bat or Bowl first?
Bat
Bowl


We all love gift, it is something make us happy. But the happiness when you give it is much more than when you take.


This Mosfet Characterization model is one of the five benchmark circuits described in the SPICE3 Version e3 User's Manual (see information of package Spice3).
This circuit is a very simple one than consists of an NMOS transistor level 1 that is connected to voltage sources at gate and drain node whereas the drain voltage source supplies the operating voltage. The user is recommended to simulate from t=0 to t=1e-7s and observe the gate node voltage ("VGS.p.v") and the transistor current ("M1.D.i"). It can be seen that the current is increasing with increasing gate voltage which means the conductivity of the transistor is increasing. The opposite case occurs for decreasing gate voltage.
Original SPICE3 netlist of the MOSFET characterization circuit:
MOS OUTPUT CHARACTERISTICS
.OPTIONS NODE NOPAGE
VDS 3 0
VGS 2 0
M1 1 2 0 0 MOD1 L=4U W=6U AD=10P AS=10P
*VIDS MEASURES ID, WE COULD HAVE USED VDS, BUT IT WOULD BE NEGATIVE VIDS 3 1 .MODEL MOD1 NMOS VTO=-2 NSUB=1.0E15 UO=550
.DC VDS 0 10 .5 VGS 0 5 1
.END
| Name | Description |
|---|---|
| SpiceConstants |


--- layout: home title: Home description: Andrew Stiefel writes and publishes a newsletter called Margin Notes. This digital garden is a collection of essasys and short notes exploring topic including product marketing, career growth, and learning. ---
Hi, I'm Andrew Stiefel.
This website is my digital garden, a slowly growing collection of working notes and essays about things I’ve created or learned over the years.
I tend to write about whatever I’m currently working on or thinking about at the time. Practically, you can usually expect something about:
If you wish to receive my latest posts, you can subscribe below or grab the RSS feed. Thanks for stopping by!


No partisan appointments to new Saskatchewan trade offices: minister
by The Canadian PressJeremy Harrison says hiring decisions will be made by the public service, and taxpayers won't see the trade offices filled with former politicians or large donors to the Saskatchewan Party.
REGINA — Saskatchewan’s trade minister says the government won’t be making partisan staff appointments to three new international offices.
Jeremy Harrison says hiring decisions will be made by the public service, and taxpayers won’t see the trade offices filled with former politicians or large donors to the Saskatchewan Party.
Premier Scott Moe on Thursday announced new offices in Japan, India and Singapore as part of a plan to expand exports and strengthen trade relationships.
Harrison says the move is proof that Saskatchewan is asserting more provincial autonomy.
“As dependent as we are on international trade, we need to be in these markets because we have seen things go the wrong way,” he said Friday.
The province currently has one office in Shanghai that’s budgeted to cost around $335,000 this year. It’s staffed with one government employee, as well as a local assistant and business development officer.
A government spokesman has said opening the three new centres is expected to cost a total of about $4.2 million.
Harrison is to leave this weekend on a trade mission to India and is to be accompanied by former Conservative prime minister Stephen Harper, whose consulting company the province has hired on a one-year contract for $240,000.
Harrison said Harper’s experience in trade negotiations will benefit Saskatchewan as it tries to maintain and expand its relationships overseas.
He said there’s been a decline in Saskatchewan exports to India to about $650 million last year from about $2 billion in 2015.
Harrison has criticized current Prime Minister Justin Trudeau’s 2018 trip to India as hurting the province. He suggested it factored into Saskatchewan’s decision to assert itself more strongly internationally.
“I know whether I would rather have with us on this mission to India the former or current prime minister accompanying us.”


My Go Resolutions for 2017
Posted on Wednesday, January 18, 2017.
’Tis the season for resolutions, and I thought it would make sense to write a little about what I hope to work on this year as far as Go is concerned.
My goal every year is to help Go developers. I want to make sure that the work we do on the Go team has a significant, positive impact on Go developers. That may sound obvious, but there are a variety of common ways to fail to achieve that: for example, spending too much time cleaning up or optimizing code that doesn’t need it; responding only to the most common or recent complaints or requests; or focusing too much on short-term improvements. It’s important to step back and make sure we’re focusing our development work where it does the most good.
This post outlines a few of my own major focuses for this year. This is only my personal list, not the Go team’s list.
One reason for posting this is to gather feedback. If these spark any ideas or suggestions of your own, please feel free to comment below or on the linked GitHub issues.
Another reason is to make clear that I’m aware of these issues as important. I think too often people interpret lack of action by the Go team as a signal that we think everything is perfect, when instead there is simply other, higher priority work to do first.
Type aliases
There is a recurring problem with moving types from one package to another during large codebase refactorings. We tried to solve it last year with general aliases, which didn’t work for at least two reasons: we didn’t explain the change well enough, and we didn’t deliver it on time, so it wasn’t ready for Go 1.8. Learning from that experience, I gave a talk and wrote an article about the underlying problem, and that started a productive discussion on the Go issue tracker about the solution space. It looks like more limited type aliases are the right next step. I want to make sure those land smoothly in Go 1.9. #18130.
Package management
I designed the Go support for downloading published packages (“goinstall”, which became “go get”) in February 2010. A lot has happened since then. In particular, other language ecosystems have really raised the bar for what people expect from package management, and the open source world has mostly agreed on semantic versioning, which provides a useful base for inferring version compatibility. Go needs to do better here, and a group of contributors have been working on a solution. I want to make sure these ideas are integrated well into the standard Go toolchain and to make package management a reason that people love Go.
Build improvements
There are a handful of shortcomings in the design of the go command’s build system that are overdue to be fixed. Here are three representative examples that I intend to address with a bit of a redesign of the internals of the go command.
Builds can be too slow,
because the go command doesn’t cache build results as aggressively as it should.
Many people don’t realize that go
install
saves its work while go
build
does not,
and then they run repeated go
build
commands that are slow
because the later builds do more work than they should need to.
The same for repeated go
test
without go
test
-i
when dependencies are modified.
All builds should be as incremental as possible.
#4719.
Test results should be cached too: if none of the inputs to a test have changed, then usually there is no need to rerun the test. This will make it very cheap to run “all tests” when little or nothing has changed. #11193.
Work outside GOPATH should be supported nearly as well
as work inside GOPATH.
In particular, it should be possible to git
clone
a repo,
cd
into it, and run go
commands and have them work fine.
Package management only makes that more important:
you’ll need to be able to work on different versions of a package (say, v1 and v2)
without having entirely separate GOPATHs for them.
#17271.
Code corpus
I think it helped to have concrete examples from real projects in the talk and article I prepared about codebase refactoring (see above). We’ve also defined that additions to vet must target problems that happen frequently in real programs. I’d like to see that kind of analysis of actual practice—examining the effects on and possible improvements to real programs—become a standard way we discuss and evaluate changes to Go.
Right now there’s not an agreed-upon representative corpus of code to use for those analyses: everyone must first create their own, which is too much work. I’d like to put together a single, self-contained Git repo people can check out that contains our official baseline corpus for those analyses. A possible starting point could be the top 100 Go language repos on GitHub by stars or forks or both.
Automatic vet
The Go distribution ships with this powerful tool,
go
vet
,
that points out correctness bugs.
We have a high bar for checks, so that when vet speaks, you should listen.
But everyone has to remember to run it.
It would be better if you didn’t have to remember.
In particular, I think we could probably run vet
in parallel with the final compile and link of the test binary
during go
test
without slowing the compile-edit-test cycle at all.
If we can do that, and if we limit the enabled vet checks to a subset
that is essentially 100% accurate,
we can make passing vet a precondition for running a test at all.
Then developers don’t need to remember to run go
vet
.
They run go
test
,
and once in a while vet speaks up with something important
and avoids a debugging session.
#18084,
#18085.
Errors & best practices
Part of the intended contract for error reporting in Go is that functions include relevant available context, including the operation being attempted (such as the function name and its arguments). For example, this program:
err := os.Remove("/tmp/nonexist")
fmt.Println(err)
prints this output:
remove /tmp/nonexist: no such file or directory
Not enough Go code adds context like os.Remove
does. Too much code does only
if err != nil {
return err
}
all the way up the call stack,
discarding useful context that should be reported
(like remove
/tmp/nonexist:
above).
I would like to try to understand whether our expectations
for including context are wrong, or if there is something
we can do to make it easier to write code that returns better errors.
There are also various discussions in the community about agreed-upon interfaces for stripping error context. I would like to try to understand when that makes sense and whether we should adopt an official recommendation.
Context & best practices
We added the new context package in Go 1.7 for holding request-scoped information like timeouts, cancellation state, and credentials. An individual context is immutable (like an individual string or int): it is only possible to derive a new, updated context and pass that context explicitly further down the call stack or (less commonly) back up to the caller. The context is now carried through APIs such as database/sql and net/http, mainly so that those can stop processing a request when the caller is no longer interested in the result. Timeout information is appropriate to carry in a context, but—to use a real example we removed—database options are not, because they are unlikely to apply equally well to all possible database operations carried out during a request. What about the current clock source, or logging sink? Is either of those appropriate to store in a context? I would like to try to understand and characterize the criteria for what is and is not an appropriate use of context.
Memory model
Go’s memory model is intentionally low-key, making few promises to users, compared to other languages. In fact it starts by discouraging people from reading the rest of the document. At the same time, it demands more of the compiler than other languages: in particular, a race on an integer value is not sufficient license for your program to misbehave in arbitrary ways. But there are some complete gaps, in particular no mention of the sync/atomic package. I think the core compiler and runtime developers all agree that the behavior of those atomics should be roughly the same as C++ seqcst atomics or Java volatiles, but we still need to write that down carefully in the memory model, and probably also in a long blog post. #5045, #7948, #9442.
Immutability
The race detector
is one of Go’s most loved features.
But not having races would be even better.
I would love it if there were some reasonable way to integrate
reference immutability into Go,
so that programmers can make clear, checked assertions about what can and cannot
be written and thereby eliminate certain races at compile time.
Go already has one immutable type, string
; it would
be nice to retroactively define that
string
is a named type (or type alias) for immutable
[]byte
.
I don’t think that will happen this year,
but I’d like to understand the solution space better.
Javari, Midori, Pony, and Rust have all staked out interesting points
in the solution space, and there are plenty of research papers
beyond those.
In the long-term, if we could statically eliminate the possibility of races, that would eliminate the need for most of the memory model. That may well be an impossible dream, but again I’d like to understand the solution space better.
Generics
Nothing sparks more heated arguments among Go and non-Go developers than the question of whether Go should have support for generics (or how many years ago that should have happened). I don’t believe the Go team has ever said “Go does not need generics.” What we have said is that there are higher-priority issues facing Go. For example, I believe that better support for package management would have a much larger immediate positive impact on most Go developers than adding generics. But we do certainly understand that for a certain subset of Go use cases, the lack of parametric polymorphism is a significant hindrance.
Personally, I would like to be able to write general channel-processing functions like:
// Join makes all messages received on the input channels
// available for receiving from the returned channel.
func Join(inputs ...<-chan T) <-chan T
// Dup duplicates messages received on c to both c1 and c2.
func Dup(c <-chan T) (c1, c2 <-chan T)
I would also like to be able to write Go support for high-level data processing abstractions, analogous to FlumeJava or C#’s LINQ, in a way that catches type errors at compile time instead of at run time. There are also any number of data structures or generic algorithms that might be written, but I personally find these broader applications more compelling.
We’ve struggled off and on
for years
to find the right way to add generics to Go.
At least a few of the past proposals got hung up on trying to design
something that provided both general parametric polymorphism
(like chan
T
) and also a unification of string
and []byte
.
If the latter is handled by parameterization over immutability,
as described in the previous section, then maybe that simplifies
the demands on a design for generics.
When I first started thinking about generics for Go in 2008, the main examples to learn from were C#, Java, Haskell, and ML. None of the approaches in those languages seemed like a perfect fit for Go. Today, there are newer attempts to learn from as well, including Dart, Midori, Rust, and Swift.
It’s been a few years since we ventured out and explored the design space. It is probably time to look around again, especially in light of the insight about mutability and the additional examples set by newer languages. I don’t think generics will happen this year, but I’d like to be able to say I understand the solution space better.


Teaching Kotlin with EduTools plugin
With EduTools plugin you can learn and teach Kotlin through code practicing tasks. It is available both in Android Studio and IntelliJ IDEA. This tutorial describes course creation for an educator. If you want to use EduTools plugin for learning, read "Learning Kotlin with EduTools plugin".
Installing EduTools plugin
Go to Preferences -> Plugins (or Configure -> Plugins from the Welcome Screen), press Install JetBrains Plugin… and search for EduTools plugin:
When installed for the first time, EduTools plugin asks if you're a Learner or an Educator. Please choose Educator to enable course creation functionality:
If you need to enable the course creation functionality later, you can go to Help Menu -> Enable Course Creator Features.
Creating a course
To create a course, go to Create New Course from the Welcome Screen or in the File Menu, fill in the title, author and description:
Every Kotlin course created with EduTools is structured as a list of lessons. Each lesson contains tasks. A task has the following components: a description, a Task.kt
file with exercise code, a Tests.kt
file with the task check, and any other files needed for the learner to complete the task.
Go to View -> Tool Windows -> Project to look at the course structure:
Add more lessons and tasks via the File Menu -> New, drag-n-drop items to reorder them, or to rename items right-click them then in the menu use Refactor -> Rename:
If you need additional files for the task, just add them and decide whether you want to make them visible to learners or not with the Course Creator -> Make Visible to / Hide from Student.
Working with tasks
Open Task.kt
file to write the code you want for the exercise:
Once the code is ready, select a fragment you want a learner to fill in, invoke Add Answer Placeholder command from the right-click menu and add the placeholder text and some hints to help the learner find the right solution:
If you want to see how your task will be look to the learner, right-click the Task.kt
file in the Project tool window, and choose Course Creator -> Show Preview:
Now it's time to add the task description. Click the Edit icon at the top of Task Description panel to switch to the editing mode:
EduTools supports HTML and Markdown, so you can start editing with your preferred format, or even a combination of both.
Writing tests
You can write your custom tests using EduTools testing framework to automatically verify the learner's solution. Every task has a Tests.kt
file already filled in with a test template that you will need to modify:
Let's replace testSolution()
function content with the following:
To check that your code passes your own test click the Check icon at the top of Task Description panel. If your code and test are correct, you will see the "Congratulations!" text:
Course preview
When you have finished creating your course, it's a good idea to view your course from a learner's perspective and test it. Right-click on the course view and go to Course Creator -> Create Course Preview to open your course in learner mode:
So you can solve tasks:
And verify them:
Course sharing
You can share your learning materials publicly or privately with your students or co-workers on Stepik, a learning management and MOOC platform. Or you can save your course as an archive file.
To save your course as a zip file, go to Course Creator -> Generate Course Archive action. The generated archive can be opened via the Browse Courses available from the Welcome Screen and in the File Menu with the Import Course icon:
If you have an account on Stepik, you can easily upload your course there, update it anytime you need, and share publicly or privately.


I have over 7 years of experience as lawyer, having the Master degree in Law. Now I am willing to make a change in my career and to start coding, which seems to be a really interesting job to me.
I also want to mention that now I'm learning with BOOGEEK. Hope to reach this course to the end and become an IT proffessional.
View ResumeA front-end web developer is responsible for implementing visual and interactive elements that users engage with through their web browser when using a web application. They are usually supported by back-end web developers, who are responsible for server-side application logic and integration of the work front-end developers do.
The Quality Assurance Specialist's responsibilities include developing and implementing quality assurance policies, conducting tests and inspections, identifying production, process, or product issues, and presenting solutions.
A Structured Query Language (SQL) developer is a database specialist tasked with developing and maintaining systems to store, organize, and access data. This is a highly technical role that incorporates aspects of computer programming, software engineering, and data architecture and management. SQL developers analyze an organization’s data management, input, and security needs and design tools that support data access and information security.
+37369-572-118
Call to Action